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It is now prohibited to replace a component instance’s root $data. This prevents some edge cases in the reactivity system and makes the component state more predictable (especially with type-checking systems).

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Instead, retrieve reactive data directly.

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Use the native DOM API:

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Run the
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Run the
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Or if
myElement
is the last child:

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Run the
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No real use. If you do happen to rely on this feature somehow and aren’t sure how to work around it, post on
the forum
for ideas.

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Run the
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Upgrade Path

Run the
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console errors
.

Upgrade Path

Run the
migration helper
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console errors
.

Components now always replace the element they’re bound to. To simulate the behavior of
replace: false
, you can wrap your root component with an element similar to the one you’re replacing. For example:

Contributed by Ignacio Rodriguez-Iturbe, March 6, 2012 (sent for review November 22, 2011)

Abstract

Mathematical models can provide key insights into the course of an ongoing epidemic, potentially aiding real-time emergency management in allocating health care resources and by anticipating the impact of alternative interventions. We study the ex post reliability of predictions of the 2010–2011 Haiti cholera outbreak from four independent modeling studies that appeared almost simultaneously during the unfolding epidemic. We consider the impact of different approaches to the modeling of spatial spread of and mechanisms of cholera transmission, accounting for the dynamics of susceptible and infected individuals within different local human communities. To explain resurgences of the epidemic, we go on to include waning immunity and a mechanism explicitly accounting for rainfall as a driver of enhanced disease transmission. The formal comparative analysis is carried out via the Akaike information criterion (AIC) to measure the added information provided by each process modeled, discounting for the added parameters. A generalized model for Haitian epidemic cholera and the related uncertainty is thus proposed and applied to the year-long dataset of reported cases now available. The model allows us to draw predictions on longer-term epidemic cholera in Haiti from multiseason Monte Carlo runs, carried out up to January 2014 by using suitable rainfall fields forecasts. Lessons learned and open issues are discussed and placed in perspective. We conclude that, despite differences in methods that can be tested through model-guided field validation, mathematical modeling of large-scale outbreaks emerges as an essential component of future cholera epidemic control.

As a major cholera epidemic spread through Haiti (
1
⇓
⇓
⇓
⇓
–
6
), leading to 170,000 reported cases and 3,600 deaths at the end of 2010 (
1
), four independent modeling studies (
7
⇓
⇓
–
10
) appeared almost simultaneously, each predicting the subsequent course of the epidemic and/or the impact of potential management strategies.